Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	50,187
2	South Dakota	44,922
3	Louisiana	39,537
4	Mississippi	38,896
5	Alabama	37,796
6	Iowa	37,145
7	Wisconsin	36,430
8	Florida	36,410
9	Tennessee	35,986
10	Arkansas	35,365
11	Georgia	34,309
12	Idaho	33,798
13	Nebraska	33,346
14	South Carolina	33,309
15	Utah	33,089
16	Arizona	32,861
17	Texas	31,610
18	Nevada	31,274
19	Illinois	30,389
20	Oklahoma	29,668
21	Rhode Island	29,285
22	Missouri	28,958
23	Kansas	27,622
24	Montana	26,740
25	New Jersey	26,060
26	New York	25,773
27	North Carolina	24,995
28	Delaware	24,819
29	Indiana	24,807
30	Minnesota	24,013
31	District of Columbia	23,821
32	Maryland	23,377
33	California	23,124
34	Kentucky	22,375
35	Massachusetts	22,020
36	Virginia	20,417
37	New Mexico	20,309
38	Wyoming	19,830
39	Puerto Rico	19,768
40	Alaska	19,569
41	Connecticut	19,100
42	Michigan	17,986
43	Ohio	17,129
44	Colorado	16,963
45	Pennsylvania	15,720
46	Washington	14,234
47	West Virginia	12,400
48	Hawaii	10,493
49	Oregon	10,065
50	New Hampshire	7,646
51	Maine	4,652
52	Vermont	3,338

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,006
2	South Dakota	956
3	Wisconsin	666
4	Montana	617
5	Alaska	569
6	Wyoming	536
7	Utah	468
8	Idaho	432
9	Tennessee	397
10	Illinois	393
11	New Mexico	384
12	Iowa	380
13	Nebraska	343
14	Indiana	339
15	Colorado	325
16	Minnesota	325
17	Missouri	318
18	Alabama	299
19	Oklahoma	298
20	Kentucky	292
21	Arkansas	286
22	Rhode Island	285
23	Kansas	268
24	Michigan	250
25	Nevada	247
26	Puerto Rico	224
27	Georgia	218
28	Mississippi	211
29	North Carolina	210
30	Ohio	207
31	Texas	205
32	Connecticut	191
33	South Carolina	191
34	Massachusetts	168
35	Delaware	164
36	New Jersey	159
37	Florida	158
38	West Virginia	154
39	Arizona	150
40	Pennsylvania	145
41	Maryland	118
42	Virginia	116
43	Washington	96
44	California	95
45	District of Columbia	95
46	Oregon	86
47	Louisiana	85
48	New York	83
49	New Hampshire	69
50	Hawaii	56
51	Maine	39
52	Vermont	35

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,834
2	New York	1,700
3	Massachusetts	1,433
4	Connecticut	1,287
5	Louisiana	1,259
6	Rhode Island	1,117
7	Mississippi	1,096
8	District of Columbia	909
9	Arizona	807
10	Illinois	774
11	Florida	765
12	Michigan	756
13	South Carolina	742
14	Georgia	719
15	Delaware	703
16	Pennsylvania	682
17	Maryland	678
18	Texas	623
19	Indiana	615
20	North Dakota	611
21	Arkansas	607
22	Alabama	584
23	Nevada	568
24	Iowa	525
25	Missouri	467
26	New Mexico	465
27	Tennessee	458
28	Ohio	446
29	California	440
30	Minnesota	426
31	South Dakota	423
32	Virginia	419
33	North Carolina	400
34	Colorado	391
35	New Hampshire	349
36	Kansas	335
37	Kentucky	331
38	Idaho	325
39	Washington	318
40	Oklahoma	316
41	Nebraska	314
42	Wisconsin	313
43	Montana	284
44	Puerto Rico	251
45	West Virginia	236
46	Utah	179
47	Oregon	155
48	Hawaii	149
49	Wyoming	133
50	Maine	108
51	Vermont	92
52	Alaska	87

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	9
2	South Dakota	7
3	Montana	6
4	Arkansas	5
5	Missouri	5
6	Wyoming	5
7	Tennessee	4
8	Delaware	2
9	Idaho	2
10	Illinois	2
11	Indiana	2
12	Iowa	2
13	Louisiana	2
14	Massachusetts	2
15	Michigan	2
16	Minnesota	2
17	Mississippi	2
18	Nebraska	2
19	New Mexico	2
20	Rhode Island	2
21	South Carolina	2
22	Texas	2
23	Wisconsin	2
24	Colorado	1
25	Connecticut	1
26	Florida	1
27	Georgia	1
28	Kentucky	1
29	Maryland	1
30	Nevada	1
31	North Carolina	1
32	Oklahoma	1
33	Pennsylvania	1
34	Puerto Rico	1
35	Virginia	1
36	Washington	1
37	Alabama	0
38	Alaska	0
39	Arizona	0
40	California	0
41	District of Columbia	0
42	Hawaii	0
43	Kansas	0
44	Maine	0
45	New Hampshire	0
46	New Jersey	0
47	New York	0
48	Ohio	0
49	Oregon	0
50	Utah	0
51	Vermont	0
52	West Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	177,058	1	99
Chattahoochee	Georgia	161,823	2	99
Trousdale	Tennessee	157,391	3	99
Lafayette	Florida	151,152	4	99
Lake	Tennessee	140,678	5	99
Davidson	Tennessee	48,548	278	91
Richland	South Carolina	41,146	471	85
York	South Carolina	23,532	1515	51
Orange	California	19,077	1916	39
Pierce	Washington	12,289	2490	20

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Jerauld	South Dakota	5,961	1	99
Emporia city	Virginia	5,238	2	99
Hancock	Georgia	5,203	3	99
Kenedy	Texas	4,950	4	99
Galax city	Virginia	4,727	5	99
Richland	South Carolina	649	841	73
Davidson	Tennessee	494	1153	63
Orange	California	456	1252	60
York	South Carolina	367	1497	52
Pierce	Washington	274	1799	42

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.